statistics for data science
Central Limit Theorem: This theorem states that regardless of the distribution of your data, if you take enough random samples of it, the sample means will follow a normal distribution.
Standard Deviation: A measure of how spread out your data is from the mean. The larger the standard deviation, the more spread out your data is.
Confidence Intervals: A range of values that likely contains the true population parameter. The level of confidence is typically set at 95%.
Hypothesis Testing: A process of evaluating whether a certain statement about a population parameter is likely true or false based on sample data.
Regression Analysis: A statistical method used to determine the relationship between a dependent variable and one or more independent variables.
ANOVA: Analysis of Variance is a statistical test used to determine whether there are any statistically significant differences between the means of two or more groups.
P-value: The probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming the null hypothesis is true.
Correlation: A measure of the relationship between two variables. Correlation can be positive (as one variable increases, so does the other), negative (as one variable increases, the other decreases), or zero (there is no relationship).
Outliers: Data points that are significantly different from the rest of the data. Outliers can have a large impact on the results of statistical analysis.
Sampling Bias: Occurs when the sample data is not representative of the population being studied. This can lead to incorrect conclusions being drawn from the data.
International Conference on Statistical Methods for Analyzing Engineering Data
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